Abstract
Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPS-AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD.
Original language | English |
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Title of host publication | Proceedings of 40th International Conference on Machine Learning, ICML 2023 |
Editors | Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett |
Publisher | ML Research Press |
Pages | 41429-41451 |
Number of pages | 23 |
Publication status | Published - Jul 2023 |
Event | 40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States Duration: 23 Jul 2023 → 29 Jul 2023 https://icml.cc/Conferences/2023 https://proceedings.mlr.press/v202/ https://openreview.net/group?id=ICML.cc/2023/Conference |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | ML Research Press |
Volume | 202 |
ISSN (Print) | 2640-3498 |
Conference
Conference | 40th International Conference on Machine Learning, ICML 2023 |
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Country/Territory | United States |
City | Honolulu |
Period | 23/07/23 → 29/07/23 |
Internet address |
Scopus Subject Areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability